Context-Based Image Selection for Electronic Media

1. A computer-implemented method, comprising: receiving, from a user device, an image request for an image that is associated with an engagement metric of a client that is unassociated with the user device; identifying, with a machine-learning engine and based on the engagement metric of the client and on an interaction data associated with a previous presentation to multiple users of a plurality of images stored in an image database, a selected image from a plurality of images stored in an image database; providing the selected image to the user device; causing an application in the user device to display the selected image, wherein the engagement metric comprises a purchase history of the client, and identifying the selected image comprises biasing a computational node in the machine-learning engine according to the engagement metric; requesting, from a camera in the user device, an image of a user's face looking at the selected image; and updating the engagement metric and the interaction data based on where the user is looking and a user's expression in the image of the user's face.

2. The computer-implemented method of claim 1 , wherein the image request comprises information associated with an image link previously provided to a client server of the client.

3. The computer-implemented method of claim 1 , wherein the engagement metric includes one or more metrics selected from the group consisting of: increasing a time a user device displays a web page associated with the client, causing a customer to click on a specific location on the web page associated with the client, causing the customer to click a purchase option on the web page associated with the client, causing the customer to provide customer information to the client, causing the customer to view an advertisement, and causing the customer to click on the advertisement.

4. The computer-implemented method of claim 1 , further comprising receiving the engagement metric from the client prior to receiving the image request from the user device.

5. The computer-implemented method of claim 1 , further comprising: responsive to the image request, obtaining information associated with a user of the user device from a third party, wherein identifying the selected image comprises identifying the selected image with the machine-learning engine and based on the engagement metric of the client and the information associated with the user.

6. The computer-implemented method of claim 2 , wherein the image link comprises an image link for obtaining the image for inclusion in a digital media of the client.

7. The computer-implemented method of claim 2 , further comprising receiving information associated with the user device with a tracking element that was previously provided to the client server of the client.

8. The computer-implemented method of claim 6 , wherein the digital media of the client comprises a web page, an email, a digital flyer, or a user interface of an application.

9. The computer-implemented method of claim 7 , wherein identifying the selected image comprises identifying the selected image with the machine-learning engine and based on the engagement metric of the client and the information associated with the user device.

10. The computer-implemented method of claim 7 , wherein the information associated with the user device comprises engagement information, provided from the client server using the tracking element, indicating engagement at the user device with the selected image.

11. The computer-implemented method of claim 9 , wherein the information associated with the user device comprises a location of the user device.

12. The computer-implemented method of claim 9 , wherein the information associated with the user device comprises information associated with a user of the user device.

13. The computer-implemented method of claim 10 , further comprising providing the selected image, the engagement metric, and the information indicating the engagement at the user device with the selected image, as training data to the machine-learning engine.

14. A computer-implemented method, comprising: storing, in an image database, a plurality of images; receiving a request from a client server for an image performance unit; receiving one or more engagement metrics for the image performance unit from the client server; generating an image link for the image performance unit; generating a code snippet to provide the one or more engagement metrics to a machine-learning engine having access to information associated with the plurality of images, responsive to an engagement with the image link that comprises an interaction data associated with a previous presentation to multiple users of the plurality of images stored in an image database; providing the image performance unit including the image link to the client server; causing an application in a user device to display an image in the image link, wherein the engagement metric comprises a purchase history of a user, and generating a code snippet comprises biasing a computational node in the machine-learning engine according to the purchase history of the user; receiving, from a camera in the user device, an image of a user's face looking at the image; and updating the engagement metric and the interaction data based on where the user is looking and a user's expression in the image of the user's face.

15. The computer-implemented method of claim 14 , further comprising: receiving an indication of an engagement, at a user device, with the image link; providing, responsive to the indication and using the code snippet, the one or more engagement metrics to the machine-learning engine; identifying, with the machine-learning engine and based on the one or more engagement metrics, a selected image from the plurality of images; and providing the selected image to the user device.

16. The computer-implemented method of claim 14 , further comprising: receiving client information from the client server, wherein generating the code snippet to provide the one or more engagement metrics to the machine-learning engine having access to information associated with the plurality of images, responsive to the engagement with the image link, comprises generating the code snippet to provide the one or more engagement metrics and the client information to the machine-learning engine having access to information associated with the plurality of images.

17. The computer-implemented method of claim 14 , wherein the one or more engagement metrics include one or more metrics selected from the group consisting of: increasing a time a user device displays a web page associated with the client server, causing a customer to click on a specific location on the web page associated with the client server, causing the customer to click a purchase option on the web page associated with the client server, causing a customer to provide customer information to the client server, causing the customer to view an advertisement, and causing the customer to click on the advertisement.

18. The computer-implemented method of claim 15 , further comprising: generating a tracking element for the image performance unit, wherein providing the image performance unit including the image link to the client server comprises providing the image performance unit including the image link and the tracking element to the client server.

19. The computer-implemented method of claim 18 , further comprising receiving engagement information associated with activity at the user device from the client server using the tracking element.

20. A computer-implemented method, comprising: providing, from an image server having an image database storing a plurality of images, an image link for inclusion in an advertisement of a client; receiving, at the image server, an image request for an image associated with the image link; identifying, with a machine-learning engine at the image server and based on an engagement metric of the client and on an interaction data associated with a previous presentation to multiple users of a plurality of images stored in an image database, a selected image for the advertisement from the plurality of images; providing the selected image, wherein the engagement metric comprises a purchase history of the client, and identifying the selected image comprises biasing a computational node in the machine-learning engine according to a purchase history of the client; causing an application in a user device to display the selected image; receiving, from a camera in the user device, an image of a user's face looking at the selected image; and updating the engagement metric and the interaction data based on where the user is looking and a user's expression in the image of the user's face.

21. The computer-implemented method of claim 20 , wherein providing the image link comprises providing the image link to an advertisement server for inclusion in the advertisement.

22. The computer-implemented method of claim 20 , wherein receiving the image request comprises receiving the image request from an advertisement server, and wherein providing the selected image comprises providing the selected image to the advertisement server.

23. The computer-implemented method of claim 20 , wherein receiving the image request comprises receiving the image request from a user device that is rendering the advertisement, and wherein providing the image comprises providing the image to the user device.

24. The computer-implemented method of claim 20 , wherein providing the image link comprises providing the image link to a client server of the client.